The COVID-19 outbreak is impacting all businesses globally. It’s impacting staff, contractors, and staffing levels; it’s impacting the supply chain; and it’s impacting processes. Despite companies having crisis management and business continuity plans, current decision-making tools and dashboards are not adequate for dealing with COVID-19-related decision-making given the geographic scale of the disruption. Established risk management methodologies and approaches tend to be static in nature and lead to models that are backward-looking. Many individuals at the senior management level now believe that these frameworks are just not up to the task — and in a number of cases this was demonstrated through the COVID-19 crisis.

New risk models — ones that look ahead by utilizing AI and machine learning, and that can be continually updated as more data becomes available — will enable organizations to truly understand and rapidly respond to the changing business landscape. It’s organizations that embrace these new tools that will return to “normal operations” at just the right time and with the right resources.

Now is the time for risk managers to focus on real-time analytics and proactive loss prevention, rather than administering risk systems related to operational processes. In Technology for Enabling Risk-based Decision Making, Part 2 of this 3 part webinar series recorded on Thursday, May 14, Cutter Consortium Senior Consultants Tom Teixeira and Craig Wylie will provide an overview of the latest trends in risk-based thinking, the technology for enabling that thinking, and risk-driven operational planning. 

• The use of AI/ML to enable risk-based thinking
• The tools and expertise do you need to have in place
• What the output means: stochastic vs indicative vs predictive
• What capability are needed to support using AI/ML tools

Guests: Register below to view this 15-minute presentation followed by 5-10 minutes of Q&A, on-demand. This quick overview will give you the insight you need to jump start your risk management program and begin exploring the opportunities that AI and ML-based risk models can reveal.